The overall aim of this project is to improve our understanding of the ab initio structure prediction of single chain polypeptides. Based upon our recent advances in (1) free energy calculations of solvated oligopeptines (Klepeis and Floudas, 1999); (b) deterministic global optimization through the alphaBB approach (Adjiman et al., 1998a,b; Floudas, 2000) and its application to solvated oligopeptides (Klepeis et aL, 1998; Klepeis and Floudas, 1999b); and (c) structure refinement of oligepeptides through sparse NMR restraints (Klepeis et al., 1999), the proposed research is directed at determining the secondary and tertiary structure of polypeptides from the sequence of amino acids without using information available from the databases. We plan to focus on the following objectives: (a) Study a new approach for the identification of the helical segments in monomeric polypeptides that is based on entropic and free energy calculations of overlapping pentapeptides, and treatment of the electrostatics through the nonlinear Poisson-Boltzmann equation. (b) Investigate a novel approach, based upon a mixed-integer optimization framework, for the determination of antiparallel, parallel and multistanded beta sheets, as well as the identification of the disulfide bridges. (c) Study a new approach, based on deterministic global optimization and torsion angle dynamics, for the structure refinement of polypeptides in the presence of sparse restraints that arise either from NmR experiments or through secondary structure information provided by approaches such as those proposed in (a) and (b). (d) Investigate a novel framework for the ab initio structure prediction of single chain polypep tides based on the advances in (a), (b) and (c). (e) Develop distributed computing algorithms for (a), (b), (c) and (d), apply them to polypeptides, and develop tools for the ab initio prediction of three-dimensional structures of polypeptides.